Constrained Tensor Decompositions for Semi-blind MIMO Detection
Conference: WSA 2019 - 23rd International ITG Workshop on Smart Antennas
04/24/2019 - 04/26/2019 at Vienna, Austria
Proceedings: WSA 2019
Pages: 6Language: englishTyp: PDF
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Authors:
Khamidullina, Liana (Communication Research Laboratory, Ilmenau University of Technology, Ilmenau, Germany & German-Russian Institute of Advanced Technologies, Kazan National Research Technical University, Kazan, Russia)
Cheng, Yao; Haardt, Martin (Communication Research Laboratory, Ilmenau University of Technology, Ilmenau, Germany)
Abstract:
In this paper we introduce a complex valued PARAllel FACtor analysis 2 (PARAFAC2) tensor decomposition to perform a semi-blind data detection and channel estimation in Multiple Input Multiple Output (MIMO) communication systems. We represent the received data in the form of a three-way tensor, with receive antennas on the first mode, data symbols in each packet on the second, and packets on the third mode. Factorizing the resulting received data tensor via the proposed complex valued PARAFAC2 decomposition enables a simultaneous estimation of the channel and the transmitted data. Moreover, the use of a few training symbols allows to find a rotation matrix resolving the ambiguity inherent in the decomposition. Extensive numerical simulations have been conducted to thoroughly evaluate the performance of the proposed semi-blind MIMO detection scheme.